Ternary Alloys Enable Efficient Production of Methoxylated Chemicals via Selective Electrocatalytic Hydrogenation of Lignin Monomers
Bibliographic record
Abstract
We explore the selective electrocatalytic hydrogenation of lignin monomers to methoxylated chemicals, of particular interest, when powered by renewable electricity. Prior studies, while advancing the field rapidly, have so far lacked the needed selectivity: when hydrogenating lignin-derived methoxylated monomers to methoxylated cyclohexanes, the desired methoxy group (−OCH3) has also been reduced. The ternary PtRhAu electrocatalysts developed herein selectively hydrogenate lignin monomers to methoxylated cyclohexanes—molecules with uses in pharmaceutics. Using X-ray absorption spectroscopy and in situ Raman spectroscopy, we find that Rh and Au modulate the electronic structure of Pt and that this modulating steers intermediate energetics on the electrocatalyst surface to facilitate the hydrogenation of lignin monomers and suppress C–OCH3 bond cleavage. As a result, PtRhAu electrocatalysts achieve a record 58% faradaic efficiency (FE) toward 2-methoxycyclohexanol from the lignin monomer guaiacol at 200 mA cm–2, representing a 1.9× advance in FE and a 4× increase in partial current density compared to the highest productivity prior reports. We demonstrate an integrated lignin biorefinery where wood-derived lignin monomers are selectively hydrogenated and funneled to methoxylated 2-methoxy-4-propylcyclohexanol using PtRhAu electrocatalysts. This work offers an opportunity for the sustainable electrocatalytic synthesis of methoxylated pharmaceuticals from renewable biomass.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".